论文标题
现实世界应用的虚拟经验：使用强化学习对视力障碍的增强学习避免人行道障碍
Virtual Experience to Real World Application: Sidewalk Obstacle Avoidance Using Reinforcement Learning for Visually Impaired
论文作者
论文摘要
找到一条没有障碍的路径，这对于安全导航至关重要。在人行道上行走时，被视线和视力障碍的人需要航行安全。在这项研究中，我们通过使用增强学习整合感官输入，在人行道上开发了辅助导航。我们通过在模拟机器人环境中进行加固学习，训练了人行道避免剂（SOAA）。人行道障碍对话代理（SOCA）是通过培训具有真实对话数据的自然语言对话代理来构建的。 SOAA与SOCA一起集成在称为增强指南（AG）的原型设备中。经验分析表明，该原型从81.29％的基本案例中提高了避免障碍的经验约5％
Finding a path free from obstacles that poses minimal risk is critical for safe navigation. People who are sighted and people who are visually impaired require navigation safety while walking on a sidewalk. In this research we developed an assistive navigation on a sidewalk by integrating sensory inputs using reinforcement learning. We trained a Sidewalk Obstacle Avoidance Agent (SOAA) through reinforcement learning in a simulated robotic environment. A Sidewalk Obstacle Conversational Agent (SOCA) is built by training a natural language conversation agent with real conversation data. The SOAA along with SOCA was integrated in a prototype device called augmented guide (AG). Empirical analysis showed that this prototype improved the obstacle avoidance experience about 5% from a base case of 81.29%
